Automated Textual Descriptions for a Wide Range of Video Events with 48 Human Actions

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks (2015, methodology): “Following their work, several succeeding approaches [26, 20, 21, 15, 3] applied similar rule-based systems to datasets with larger numbers of objects and events, in different tasks and scenarios.”
A dataset for Movie Description (2015, background): “In recent years there has been an increased interest in automatically describing images [24, 25, 36, 41, 42, 43, 47, 52, 65] and videos [8, 17, 28, 29, 30, 34, 39, 58, 66, 70] with natural language.”
Visual to Text: Survey of Image and Video Captioning (2019, background): “The following works turned their focuses to real world videos [40].”
Learning to Describe Video with Weak Supervision by Exploiting Negative Sentential Information (2015, methodology): “First, some methods craft rule-based systems in which language generation is treated as an engineering task (Kojima, Tamura, and Fukunaga 2002; Lee et al. 2008; Khan, Zhang, and Gotoh 2011a; 2011b; Hanckmann, Schutte, and Burghouts 2012; Siddharth, Barbu, and Siskind 2014).”
Translating Video Content to Natural Language Descriptions (2013, methodology): “[10, 2] extract actions, body-pose, objects and their tracks on the DARPA Mind’s eye corpus which depict 48 different verbs.”